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The knowledge city and regional application of labour insurance supply management from big data algorithms

Yanhua Wang et al.

International Journal of Technology, Policy and Management2026https://doi.org/10.1504/ijtpm.2026.152560article
AJG 1
Weight
0.50

What the paper says

This paper investigated the effectiveness of labour insurance supply management in knowledge cities and regional applications through the use of big data (BD) algorithms. It compared the Big Data Algorithm-Based Management (BDAM) model with the traditional rule-based management (RBM) model across key metrics, including efficiency, safety, innovation, sustainability, costs, and resource utilisation. Results indicated that the BDAM model significantly outperformed the RBM model, with higher resource allocation efficiency (16% to 39% vs. 17.54% to 67.71%), superior safety levels (53.49% to 62.10% vs. 29.32% to 36.57%), and better innovation and sustainability scores. Although the BDAM model incurred higher initial costs, it demonstrated cost savings over time, with costs decreasing from 18.95 to 16.25, and maintained higher resource utilisation efficiency (0.737 to 0.810 vs. 0.573 to 0.635). The study emphasised the BDAM model's flexibility, scalability, and potential for integration with other smart city components.

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https://doi.org/https://doi.org/10.1504/ijtpm.2026.152560

Or copy a formatted citation

@article{yanhua2026,
  title        = {{The knowledge city and regional application of labour insurance supply management from big data algorithms}},
  author       = {Yanhua Wang et al.},
  journal      = {International Journal of Technology, Policy and Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijtpm.2026.152560},
}

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The knowledge city and regional application of labour insurance supply management from big data algorithms

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.